{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://pythonquants.com' target='_blank'>www.pythonquants.com</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DX Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "uuid": "f087ed8b-b432-48a9-a791-451ac477c046"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "1d45b83a-110e-4c94-9404-859554d3f2d7"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../python/dxa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DX Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "uuid": "278721fb-d9a8-4270-a7d5-62f85cff52f0"
   },
   "outputs": [],
   "source": [
    "from dx_frame import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk-Neutral Discounting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "uuid": "c10b29e3-bd0a-4c3f-83ea-234cc959fa5b"
   },
   "outputs": [],
   "source": [
    "dates = [dt.datetime(2015, 1, 1), dt.datetime(2015, 7, 1), dt.datetime(2016, 1, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "uuid": "6ae9c451-fdf0-41b0-9b4e-89f57c2bcc3c"
   },
   "outputs": [],
   "source": [
    "deltas = [0.0, 0.5, 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "uuid": "ed812d8b-0e9d-47ab-8793-4bede4dfaf36"
   },
   "outputs": [],
   "source": [
    "csr = constant_short_rate('csr', 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "uuid": "1ebd8c12-ee83-46cd-a0b6-7439aa8756b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[datetime.datetime(2015, 1, 1, 0, 0), 0.951229424500714],\n",
       "       [datetime.datetime(2015, 7, 1, 0, 0), 0.9755103387657228],\n",
       "       [datetime.datetime(2016, 1, 1, 0, 0), 1.0]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr.get_discount_factors(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.49589041,  1.        ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas = get_year_deltas(dates)\n",
    "deltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "uuid": "c545e941-ad55-4975-abdd-2071c0453b0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.95122942],\n",
       "       [ 0.49589041,  0.97551034],\n",
       "       [ 1.        ,  1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr.get_discount_factors(deltas, dtobjects=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "uuid": "455e1e9e-45be-4c74-b2d3-5dac6132d568"
   },
   "outputs": [],
   "source": [
    "me_gbm = market_environment('me_gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "uuid": "3d703766-a01a-458a-8730-ab52da923737"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('initial_value', 36.)\n",
    "me_gbm.add_constant('volatility', 0.2)\n",
    "me_gbm.add_constant('final_date', dt.datetime(2015, 12, 31))\n",
    "me_gbm.add_constant('currency', 'EUR')\n",
    "me_gbm.add_constant('frequency', 'M')\n",
    "me_gbm.add_constant('paths', 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "uuid": "946be8e3-9d32-42ec-becf-7174d6d37cb7"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_curve('discount_curve', csr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DX Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sn_random_numbers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-0.8044583 ,  0.32093155],\n",
       "        [-0.02548288,  0.64432383]],\n",
       "\n",
       "       [[-0.30079667,  0.38947455],\n",
       "        [-0.1074373 , -0.47998308]]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snrn = sn_random_numbers((2, 2, 2), antithetic=False, moment_matching=False, fixed_seed=True)\n",
    "snrn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.67972865,  0.81075283],\n",
       "        [ 0.04413963,  1.52641815]],\n",
       "\n",
       "       [[-0.56512826,  0.96243813],\n",
       "        [-0.13722505, -0.96166678]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snrn = sn_random_numbers((2, 2, 2), antithetic=False, moment_matching=True, fixed_seed=True)\n",
    "snrn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.3877787807814457e-17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snrn.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometric Brownian Motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "uuid": "5e40de07-230f-4e0a-b993-4081ec791fe7"
   },
   "outputs": [],
   "source": [
    "from dx_frame import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "uuid": "b630a638-da9f-4084-9e2b-14b0e1565fd6"
   },
   "outputs": [],
   "source": [
    "me_gbm = market_environment('me_gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "uuid": "a49a07ca-f6e7-495e-acc1-edfb73b22edd"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('initial_value', 36.)\n",
    "me_gbm.add_constant('volatility', 0.2)\n",
    "me_gbm.add_constant('final_date', dt.datetime(2015, 12, 31))\n",
    "me_gbm.add_constant('currency', 'EUR')\n",
    "me_gbm.add_constant('frequency', 'M')\n",
    "  # monthly frequency (respcective month end)\n",
    "me_gbm.add_constant('paths', 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "uuid": "520e0490-b29b-441e-a2f7-ae89fc87cf0f"
   },
   "outputs": [],
   "source": [
    "csr = constant_short_rate('csr', 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "uuid": "bac36a29-b2b5-4f62-b848-7cb6f382dc9b"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_curve('discount_curve', csr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "uuid": "f9193b81-e8d5-48ea-a86a-94a15a01022f"
   },
   "outputs": [],
   "source": [
    "from geometric_brownian_motion import geometric_brownian_motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "uuid": "a00112c1-422e-4140-83e9-1388bc660b11"
   },
   "outputs": [],
   "source": [
    "gbm = geometric_brownian_motion('gbm', me_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "uuid": "a6f31102-77b0-4076-a9d6-7a92f3ffdc5e"
   },
   "outputs": [],
   "source": [
    "gbm.generate_time_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "uuid": "ac81750e-be2b-403b-8260-20822f5307a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.datetime(2015, 1, 1, 0, 0),\n",
       "       datetime.datetime(2015, 1, 31, 0, 0),\n",
       "       datetime.datetime(2015, 2, 28, 0, 0),\n",
       "       datetime.datetime(2015, 3, 31, 0, 0),\n",
       "       datetime.datetime(2015, 4, 30, 0, 0),\n",
       "       datetime.datetime(2015, 5, 31, 0, 0),\n",
       "       datetime.datetime(2015, 6, 30, 0, 0),\n",
       "       datetime.datetime(2015, 7, 31, 0, 0),\n",
       "       datetime.datetime(2015, 8, 31, 0, 0),\n",
       "       datetime.datetime(2015, 9, 30, 0, 0),\n",
       "       datetime.datetime(2015, 10, 31, 0, 0),\n",
       "       datetime.datetime(2015, 11, 30, 0, 0),\n",
       "       datetime.datetime(2015, 12, 31, 0, 0)], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm.time_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "uuid": "a480cd1f-a793-476b-bdd2-a79b568a1872"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6 ms, sys: 1 ms, total: 7 ms\n",
      "Wall time: 6.63 ms\n"
     ]
    }
   ],
   "source": [
    "%time paths_1 = gbm.get_instrument_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "uuid": "aed54761-2093-409d-912a-fded6f76b418"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 36.        ,  36.        ,  36.        , ...,  36.        ,\n",
       "         36.        ,  36.        ],\n",
       "       [ 37.40294432,  38.1202286 ,  34.39982796, ...,  36.25237338,\n",
       "         35.08385942,  39.66803061],\n",
       "       [ 39.52141274,  42.25526228,  32.4373032 , ...,  34.85854718,\n",
       "         33.65945317,  37.68717766],\n",
       "       ..., \n",
       "       [ 40.49192111,  33.44349499,  23.52000117, ...,  37.87510754,\n",
       "         30.14200933,  30.47215628],\n",
       "       [ 42.4832345 ,  36.9254123 ,  21.90666563, ...,  36.03678387,\n",
       "         30.9275866 ,  30.73817091],\n",
       "       [ 43.76599201,  37.80507666,  24.12339514, ...,  36.28631617,\n",
       "         28.15660098,  29.06263533]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paths_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "uuid": "e6dfa29f-27df-4e30-ba44-7f784425d4d9"
   },
   "outputs": [],
   "source": [
    "gbm.update(volatility=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "uuid": "d1ec64b1-5f80-4c8b-b4ef-d1cbe2622d31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6 ms, sys: 0 ns, total: 6 ms\n",
      "Wall time: 6.71 ms\n"
     ]
    }
   ],
   "source": [
    "%time paths_2 = gbm.get_instrument_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "uuid": "48e79769-db06-4973-8e3a-a8933df14b1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 735599.,  735630.,  735658.,  735689.,  735719.,  735750.,\n",
       "         735780.,  735811.,  735842.,  735872.,  735903.,  735933.]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFoOUFCA42HLnLC4mf2znzlUfa41AI4WCMgzt3m18FqKUFErTt2oVjeutDRgD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Zy4RZ5687xb/r2wx3fqFHp+Xh488ED533/0KIwaRar9/fb+1q1hyxY86enQvTupEyYEyvPzz7Bi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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e9ebc610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(8, 4))\n",
    "p1 = plt.plot(gbm.time_grid, paths_1[:, :10], 'b')\n",
    "p2 = plt.plot(gbm.time_grid, paths_2[:, :10], 'r-.')\n",
    "plt.grid(True)\n",
    "l1 = plt.legend([p1[0], p2[0]],\n",
    "                ['low volatility', 'high volatility'], loc=2)\n",
    "plt.gca().add_artist(l1)\n",
    "plt.xticks(rotation=30)\n",
    "# tag: gbm_class_fig\n",
    "# title: Simulated paths from geometric Brownian motion simulation class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jump Diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "uuid": "32912563-dcc1-4399-b5ed-1603c83f19f6"
   },
   "outputs": [],
   "source": [
    "me_jd = market_environment('me_jd', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "uuid": "66dda0df-b62e-4596-803f-3f74d0b83320"
   },
   "outputs": [],
   "source": [
    "# specific to simulation class\n",
    "me_jd.add_constant('lambda', 0.3)\n",
    "me_jd.add_constant('mu', -0.75)\n",
    "me_jd.add_constant('delta', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "uuid": "c1e390cc-dd6b-4186-a7be-012f8187ebe4"
   },
   "outputs": [],
   "source": [
    "me_jd.add_environment(me_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "uuid": "31aeb150-6007-4eaa-b70b-8a798f044f0c"
   },
   "outputs": [],
   "source": [
    "from jump_diffusion import jump_diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "uuid": "e9c72a6e-3433-442e-9db3-02abf8efaff2"
   },
   "outputs": [],
   "source": [
    "jd = jump_diffusion('jd', me_jd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "uuid": "18fd9ba5-e1c6-4e90-ac6a-fa3727acc524"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18 ms, sys: 1 ms, total: 19 ms\n",
      "Wall time: 18 ms\n"
     ]
    }
   ],
   "source": [
    "%time paths_3 = jd.get_instrument_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "uuid": "38806874-53b2-4e91-9bd5-5764654446b8"
   },
   "outputs": [],
   "source": [
    "jd.update(lamb=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "uuid": "a2bd3f5d-44cb-458d-ae7d-9238d7a6b93b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17 ms, sys: 1 ms, total: 18 ms\n",
      "Wall time: 18.4 ms\n"
     ]
    }
   ],
   "source": [
    "%time paths_4 = jd.get_instrument_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "uuid": "5b744e93-dac1-4868-8499-6a8f70bcfc42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 735599.,  735630.,  735658.,  735689.,  735719.,  735750.,\n",
       "         735780.,  735811.,  735842.,  735872.,  735903.,  735933.]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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A5z16cD5vHufvvMP5kSOcV1d3rkydBTpoHLFA0DXIyqKY2V/+Apw4QV3rbt3I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e55b6850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "p1 = plt.plot(gbm.time_grid, paths_3[:, :10], 'b')\n",
    "p2 = plt.plot(gbm.time_grid, paths_4[:, :10], 'r-.')\n",
    "plt.grid(True)\n",
    "l1 = plt.legend([p1[0], p2[0]],\n",
    "                ['low intensity', 'high intensity'], loc=3)\n",
    "plt.gca().add_artist(l1)\n",
    "plt.xticks(rotation=30)\n",
    "# tag: jd_class_fig\n",
    "# title: Simulated paths from jump diffusion simulation class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Square-Root Diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "uuid": "5cee70a6-b34b-47dd-a0ca-e4a239209f9a"
   },
   "outputs": [],
   "source": [
    "me_srd = market_environment('me_srd', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "uuid": "cce8be97-72ab-44b8-8080-74caa746f4fe"
   },
   "outputs": [],
   "source": [
    "me_srd.add_constant('initial_value', .25)\n",
    "me_srd.add_constant('volatility', 0.05)\n",
    "me_srd.add_constant('final_date', dt.datetime(2015, 12, 31))\n",
    "me_srd.add_constant('currency', 'EUR')\n",
    "me_srd.add_constant('frequency', 'W')\n",
    "me_srd.add_constant('paths', 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "uuid": "51da0da3-f606-4872-a44e-356ab708c1b7"
   },
   "outputs": [],
   "source": [
    "# specific to simualation class\n",
    "me_srd.add_constant('kappa', 4.0)\n",
    "me_srd.add_constant('theta', 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "uuid": "d88f4994-9f99-4b84-ae61-64b9ef193bfb"
   },
   "outputs": [],
   "source": [
    "# required but not needed for the class\n",
    "me_srd.add_curve('discount_curve', constant_short_rate('r', 0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "uuid": "9614dce2-b786-447f-b750-4ef4d78e1cd9"
   },
   "outputs": [],
   "source": [
    "from square_root_diffusion import square_root_diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "uuid": "2ab8a1b3-0ca9-41cc-977b-a935a6f0bec8"
   },
   "outputs": [],
   "source": [
    "srd = square_root_diffusion('srd', me_srd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "uuid": "0ae9685b-e73c-45f5-996f-58c21843234a"
   },
   "outputs": [],
   "source": [
    "srd_paths = srd.get_instrument_values()[:, :10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "uuid": "c25fa7d4-688a-4275-9224-0bc20a8b54c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 735599.,  735630.,  735658.,  735689.,  735719.,  735750.,\n",
       "         735780.,  735811.,  735842.,  735872.,  735903.,  735933.]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBw7w22+/0a9fP6Kiop7+vDVN07RX0uvdKCsFH32UZBd2tvTZqJirIm5X/53I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DB0KNC8ObBuHZB4MxE3B9xE96DuKFOtjF6alSol6m1qhJSWP2Bli7aY+++7qN7rGOpUqIRRVlb4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19ec051b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(srd.time_grid, srd.get_instrument_values()[:, :10])\n",
    "plt.axhline(me_srd.get_constant('theta'), color='r', ls='--', lw=2.0)\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=30)\n",
    "# tag: srd_class_fig\n",
    "# title: Simulated paths from square-root diffusion simulation class (dashed line = long-term mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valuation Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### European Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "uuid": "57252fe1-29a2-44bb-8cab-64337ea2bdc7"
   },
   "outputs": [],
   "source": [
    "from dx_simulation import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "uuid": "9f6df028-fd97-4dc9-9960-d5202b94feb1"
   },
   "outputs": [],
   "source": [
    "me_gbm = market_environment('me_gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "uuid": "527be3e7-3e98-4fe5-b41e-23af730c0921"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('initial_value', 36.)\n",
    "me_gbm.add_constant('volatility', 0.2)\n",
    "me_gbm.add_constant('final_date', dt.datetime(2015, 12, 31))\n",
    "me_gbm.add_constant('currency', 'EUR')\n",
    "me_gbm.add_constant('frequency', 'M')\n",
    "me_gbm.add_constant('paths', 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "uuid": "fc61c07b-7bb2-45ac-a10d-617a24747953"
   },
   "outputs": [],
   "source": [
    "csr = constant_short_rate('csr', 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "uuid": "6acd3ae9-5986-4cec-9f66-407ddd79202e"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_curve('discount_curve', csr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "uuid": "dc0c43ae-c6f0-434e-ad6e-c3fbe91b1175"
   },
   "outputs": [],
   "source": [
    "gbm = geometric_brownian_motion('gbm', me_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "uuid": "17f4b627-7fb8-43f6-bb15-26a6a4d60b0b"
   },
   "outputs": [],
   "source": [
    "me_call = market_environment('me_call', me_gbm.pricing_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "uuid": "838f54a8-e11c-4ab5-85ce-8393d89f957f"
   },
   "outputs": [],
   "source": [
    "me_call.add_constant('strike', 40.)\n",
    "me_call.add_constant('maturity', dt.datetime(2015, 12, 31))\n",
    "me_call.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "uuid": "4bfb5550-ab6b-4b56-9604-3fad05c5f334"
   },
   "outputs": [],
   "source": [
    "payoff_func = 'np.maximum(maturity_value - strike, 0)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "uuid": "5a331672-9004-43b0-9ba6-61622d4e1b65"
   },
   "outputs": [],
   "source": [
    "from valuation_mcs_european import valuation_mcs_european"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "uuid": "0ba08733-7d34-488e-b460-e5abc223a85a"
   },
   "outputs": [],
   "source": [
    "eur_call = valuation_mcs_european('eur_call', underlying=gbm,\n",
    "                        mar_env=me_call, payoff_func=payoff_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "uuid": "1869f59a-2c88-4bb4-bde6-b662edbd5f62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12 ms, sys: 1 ms, total: 13 ms\n",
      "Wall time: 12.2 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.140776"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time eur_call.present_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "uuid": "62330fe7-0fbe-4a24-886e-a9c64b61122c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6 ms, sys: 0 ns, total: 6 ms\n",
      "Wall time: 6.48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5148"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time eur_call.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "uuid": "999b5d90-57b9-46da-b7cc-981841df58b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15 ms, sys: 0 ns, total: 15 ms\n",
      "Wall time: 14.6 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.2782"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time eur_call.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "uuid": "bf5e7430-eea7-47ae-b1a6-e335c144929b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 163 ms, sys: 1 ms, total: 164 ms\n",
      "Wall time: 163 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "s_list = np.arange(34., 46.1, 2.)\n",
    "p_list = []; d_list = []; v_list = []\n",
    "for s in s_list:\n",
    "    eur_call.update(initial_value=s)\n",
    "    p_list.append(eur_call.present_value(fixed_seed=True))\n",
    "    d_list.append(eur_call.delta())\n",
    "    v_list.append(eur_call.vega())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "uuid": "70b7e095-a138-47bb-90b5-2a658d74b87c"
   },
   "outputs": [],
   "source": [
    "from plot_option_stats import plot_option_stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "uuid": "2590618e-3bfd-4416-82b6-eef269a28f38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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78MicmUF/DRERaUACn71S1LXyOjA+OuFo6AoLYePGIycVjRpBjx7e0b279zho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\nBoG1RWBf5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e4ed5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_option_stats(s_list, p_list, d_list, v_list)\n",
    "# tag: option_stats_1\n",
    "# title: Present value, Delta and Vega estimates for European call option\n",
    "# size: 75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "uuid": "2d5ca812-5448-47fe-9fd3-af2ab7eca13e"
   },
   "outputs": [],
   "source": [
    "payoff_func = 'np.maximum(0.33 * (maturity_value + max_value) - 40, 0)'\n",
    "  # payoff dependent on both the simulated maturity value\n",
    "  # and the maximum value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "uuid": "62c9340d-a2a3-477e-b640-389d81c3b8a1"
   },
   "outputs": [],
   "source": [
    "eur_as_call = valuation_mcs_european('eur_as_call', underlying=gbm,\n",
    "                            mar_env=me_call, payoff_func=payoff_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "uuid": "50f5cf9a-d2c7-4e69-9361-e3bf8678c4a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 167 ms, sys: 1 ms, total: 168 ms\n",
      "Wall time: 168 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "s_list = np.arange(34., 46.1, 2.)\n",
    "p_list = []; d_list = []; v_list = []\n",
    "for s in s_list:\n",
    "    eur_as_call.update(s)\n",
    "    p_list.append(eur_as_call.present_value(fixed_seed=True))\n",
    "    d_list.append(eur_as_call.delta())\n",
    "    v_list.append(eur_as_call.vega())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "uuid": "a4602156-ade5-49fe-97c8-3088dc47944c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3rV3Prt116NprUIWnNnbuPPq0RpcucNZZRyYVxx8fXRthBVqyZIn+A3KR4uku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\nzMzMrKz+ByhTBvDTpA7ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e5571e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_option_stats(s_list, p_list, d_list, v_list)\n",
    "# tag: option_stats_2\n",
    "# title: Present value, Delta and Vega estimates for European Asian call option\n",
    "# size: 75"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### American Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "uuid": "5d69e0ae-939b-49fa-a329-2f0538f8cb20"
   },
   "outputs": [],
   "source": [
    "from dx_simulation import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "uuid": "81d66ea7-15df-429a-9f87-fc2f2c881726"
   },
   "outputs": [],
   "source": [
    "me_gbm = market_environment('me_gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "uuid": "f822430b-6875-4b81-9c15-4684eea1225c"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('initial_value', 36.)\n",
    "me_gbm.add_constant('volatility', 0.2)\n",
    "me_gbm.add_constant('final_date', dt.datetime(2016, 12, 31))\n",
    "me_gbm.add_constant('currency', 'EUR')\n",
    "me_gbm.add_constant('frequency', 'W')\n",
    "  # weekly frequency\n",
    "me_gbm.add_constant('paths', 50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "uuid": "ed806c52-2dc7-4dec-a8dc-8e0d99e18a3c"
   },
   "outputs": [],
   "source": [
    "csr = constant_short_rate('csr', 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "uuid": "78738a9b-8f75-4710-b31d-415cdade6bb8"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_curve('discount_curve', csr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "uuid": "4b0692ba-91f6-4d19-adfd-d4b31590542b"
   },
   "outputs": [],
   "source": [
    "gbm = geometric_brownian_motion('gbm', me_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "uuid": "ab695214-a424-4ead-9c96-ef75a8f52ed0"
   },
   "outputs": [],
   "source": [
    "payoff_func = 'np.maximum(strike - instrument_values, 0)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "uuid": "558b7076-4cc6-4e45-9e04-395eb3ba8aac"
   },
   "outputs": [],
   "source": [
    "me_am_put = market_environment('me_am_put', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "uuid": "783eb5ab-6dd6-4561-9614-aedb4247d6ab"
   },
   "outputs": [],
   "source": [
    "me_am_put.add_constant('maturity', dt.datetime(2015, 12, 31))\n",
    "me_am_put.add_constant('strike', 40.)\n",
    "me_am_put.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "uuid": "dd6bc5f2-ed01-4877-a7da-dee5d9bd9c45"
   },
   "outputs": [],
   "source": [
    "from valuation_mcs_american import valuation_mcs_american"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "uuid": "a43bc735-e35e-4621-9185-5d9d415b0efd"
   },
   "outputs": [],
   "source": [
    "am_put = valuation_mcs_american('am_put', underlying=gbm,\n",
    "                    mar_env=me_am_put, payoff_func=payoff_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "uuid": "eeb5de5d-f95b-4acd-bbab-2eae998d8f46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 559 ms, sys: 22 ms, total: 581 ms\n",
      "Wall time: 580 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.470627"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time am_put.present_value(fixed_seed=True, bf=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "uuid": "671c9254-1fa5-432c-accb-4d0610abd910"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.4 s, sys: 259 ms, total: 14.7 s\n",
      "Wall time: 14.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ls_table = []\n",
    "for initial_value in (36., 38., 40., 42., 44.): \n",
    "    for volatility in (0.2, 0.4):\n",
    "        for maturity in (dt.datetime(2015, 12, 31),\n",
    "                         dt.datetime(2016, 12, 31)):\n",
    "            am_put.update(initial_value=initial_value,\n",
    "                          volatility=volatility,\n",
    "                          maturity=maturity)\n",
    "            ls_table.append([initial_value,\n",
    "                             volatility,\n",
    "                             maturity,\n",
    "                             am_put.present_value(bf=5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "uuid": "125e9a02-1db7-4fdd-bdd8-6f0f522281d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S0  | Vola | T | Value\n",
      "----------------------\n",
      "36  | 0.2  | 1 | 4.444\n",
      "36  | 0.2  | 2 | 4.769\n",
      "36  | 0.4  | 1 | 7.000\n",
      "36  | 0.4  | 2 | 8.378\n",
      "38  | 0.2  | 1 | 3.210\n",
      "38  | 0.2  | 2 | 3.645\n",
      "38  | 0.4  | 1 | 6.066\n",
      "38  | 0.4  | 2 | 7.535\n",
      "40  | 0.2  | 1 | 2.267\n",
      "40  | 0.2  | 2 | 2.778\n",
      "40  | 0.4  | 1 | 5.203\n",
      "40  | 0.4  | 2 | 6.753\n",
      "42  | 0.2  | 1 | 1.554\n",
      "42  | 0.2  | 2 | 2.099\n",
      "42  | 0.4  | 1 | 4.459\n",
      "42  | 0.4  | 2 | 6.046\n",
      "44  | 0.2  | 1 | 1.056\n",
      "44  | 0.2  | 2 | 1.618\n",
      "44  | 0.4  | 1 | 3.846\n",
      "44  | 0.4  | 2 | 5.494\n"
     ]
    }
   ],
   "source": [
    "print \"S0  | Vola | T | Value\"\n",
    "print 22 * \"-\"\n",
    "for r in ls_table:\n",
    "    print \"%d  | %3.1f  | %d | %5.3f\" % \\\n",
    "          (r[0], r[1], r[2].year - 2014, r[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "uuid": "481d5438-d32b-40a2-aa8c-687b19138e29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4631"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.update(initial_value=36.)\n",
    "am_put.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "uuid": "84cf9547-c8b3-4205-81e5-8949ae798394"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.4939"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.vega()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "uuid": "e826f896-6999-4007-b0b2-c46750e0afc4"
   },
   "outputs": [],
   "source": [
    "from dx_valuation import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "uuid": "636c1eae-142b-447f-a137-d36d81e4626b"
   },
   "outputs": [],
   "source": [
    "me_gbm = market_environment('me_gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "uuid": "6e3324f6-21ee-46f4-b330-205ab3766f1f"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('initial_value', 36.)\n",
    "me_gbm.add_constant('volatility', 0.2)\n",
    "me_gbm.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "uuid": "4133fd96-79aa-4df7-8dbb-328561e2267c"
   },
   "outputs": [],
   "source": [
    "me_gbm.add_constant('model', 'gbm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "uuid": "552c207a-7c7d-4809-a5ee-64c11d7604a3"
   },
   "outputs": [],
   "source": [
    "from derivatives_position import derivatives_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "uuid": "d2cd3a35-f2a9-470c-a705-299172dd8887"
   },
   "outputs": [],
   "source": [
    "me_am_put = market_environment('me_am_put', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "uuid": "f3bd69f3-c361-44ce-9cad-3584366b459f"
   },
   "outputs": [],
   "source": [
    "me_am_put.add_constant('maturity', dt.datetime(2015, 12, 31))\n",
    "me_am_put.add_constant('strike', 40.)\n",
    "me_am_put.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "uuid": "70aa8500-9a8a-4d54-951e-ac820eaff24d"
   },
   "outputs": [],
   "source": [
    "payoff_func = 'np.maximum(strike - instrument_values, 0)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "uuid": "ce2244bd-c4c0-4386-a518-06c3373a96b1"
   },
   "outputs": [],
   "source": [
    "am_put_pos = derivatives_position(\n",
    "             name='am_put_pos',\n",
    "             quantity=3,\n",
    "             underlying='gbm',\n",
    "             mar_env=me_am_put,\n",
    "             otype='American',\n",
    "             payoff_func=payoff_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "uuid": "9bb959eb-6ce0-48eb-9f1f-bf64eee505fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME\n",
      "am_put_pos \n",
      "\n",
      "QUANTITY\n",
      "3 \n",
      "\n",
      "UNDERLYING\n",
      "gbm \n",
      "\n",
      "MARKET ENVIRONMENT\n",
      "\n",
      "**Constants**\n",
      "strike 40.0\n",
      "maturity 2015-12-31 00:00:00\n",
      "currency EUR\n",
      "\n",
      "**Lists**\n",
      "\n",
      "**Curves**\n",
      "\n",
      "OPTION TYPE\n",
      "American \n",
      "\n",
      "PAYOFF FUNCTION\n",
      "np.maximum(strike - instrument_values, 0)\n"
     ]
    }
   ],
   "source": [
    "am_put_pos.get_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "uuid": "a7f7a55f-cf66-457a-ba2f-9c9f15697d98"
   },
   "outputs": [],
   "source": [
    "me_jd = market_environment('me_jd', me_gbm.pricing_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "uuid": "4c2da9df-820c-49bb-9da0-fa212aa8ac40"
   },
   "outputs": [],
   "source": [
    "# add jump diffusion specific parameters\n",
    "me_jd.add_constant('lambda', 0.3)\n",
    "me_jd.add_constant('mu', -0.75)\n",
    "me_jd.add_constant('delta', 0.1)\n",
    "# add other parameters from gbm\n",
    "me_jd.add_environment(me_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "uuid": "6f93e1aa-7ccb-4434-b79e-2357bb056fc9"
   },
   "outputs": [],
   "source": [
    "# needed for portfolio valuation\n",
    "me_jd.add_constant('model', 'jd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "uuid": "178e550a-e040-4ac8-9ad8-e3bbf4fdc50d"
   },
   "outputs": [],
   "source": [
    "me_eur_call = market_environment('me_eur_call', me_jd.pricing_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "uuid": "08c13651-9fbf-44bf-b9a9-e94409a6e891"
   },
   "outputs": [],
   "source": [
    "me_eur_call.add_constant('maturity', dt.datetime(2015, 6, 30))\n",
    "me_eur_call.add_constant('strike', 38.)\n",
    "me_eur_call.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "uuid": "c62a77e4-a502-4ceb-a5a3-9b3ec2be7d72"
   },
   "outputs": [],
   "source": [
    "payoff_func = 'np.maximum(maturity_value - strike, 0)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "uuid": "f1cb96fa-385a-41d6-b68d-1f80c9590370"
   },
   "outputs": [],
   "source": [
    "eur_call_pos = derivatives_position(\n",
    "             name='eur_call_pos',\n",
    "             quantity=5,\n",
    "             underlying='jd',\n",
    "             mar_env=me_eur_call,\n",
    "             otype='European',\n",
    "             payoff_func=payoff_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "uuid": "e8df55ac-a860-4dd6-88f2-c4aa15e323f9"
   },
   "outputs": [],
   "source": [
    "underlyings = {'gbm': me_gbm, 'jd' : me_jd}\n",
    "positions = {'am_put_pos' : am_put_pos, 'eur_call_pos' : eur_call_pos}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "uuid": "38a7b06d-9923-4729-bbaf-184b55930d69"
   },
   "outputs": [],
   "source": [
    "# discounting object for the valuation\n",
    "csr = constant_short_rate('csr', 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "uuid": "d8ecc803-4ea8-45b4-975f-88e716a171e6"
   },
   "outputs": [],
   "source": [
    "val_env = market_environment('general', me_gbm.pricing_date)\n",
    "val_env.add_constant('frequency', 'W')\n",
    "  # monthly frequency\n",
    "val_env.add_constant('paths', 25000)\n",
    "val_env.add_constant('starting_date', val_env.pricing_date)\n",
    "val_env.add_constant('final_date', val_env.pricing_date)\n",
    "  # not yet known; take pricing_date temporarily\n",
    "val_env.add_curve('discount_curve', csr)\n",
    "  # select single discount_curve for whole portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "uuid": "1f533a99-c881-4628-befe-ce63bbfdaafd"
   },
   "outputs": [],
   "source": [
    "from derivatives_portfolio import derivatives_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "uuid": "7a231d26-ec5f-4b0f-8c2e-d5ee4b93f0c4"
   },
   "outputs": [],
   "source": [
    "portfolio = derivatives_portfolio(\n",
    "                name='portfolio',\n",
    "                positions=positions,\n",
    "                val_env=val_env,\n",
    "                assets=underlyings,\n",
    "                fixed_seed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "uuid": "ec163641-6908-4fc8-bb75-95483021f1b4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>quant.</th>\n",
       "      <th>value</th>\n",
       "      <th>curr.</th>\n",
       "      <th>pos_value</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>pos_vega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eur_call_pos</td>\n",
       "      <td>5</td>\n",
       "      <td>2.822235</td>\n",
       "      <td>EUR</td>\n",
       "      <td>14.111175</td>\n",
       "      <td>3.3080</td>\n",
       "      <td>42.7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>am_put_pos</td>\n",
       "      <td>3</td>\n",
       "      <td>4.472021</td>\n",
       "      <td>EUR</td>\n",
       "      <td>13.416063</td>\n",
       "      <td>-2.0895</td>\n",
       "      <td>30.5181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  quant.     value curr.  pos_value  pos_delta  pos_vega\n",
       "0  eur_call_pos       5  2.822235   EUR  14.111175     3.3080   42.7900\n",
       "1    am_put_pos       3  4.472021   EUR  13.416063    -2.0895   30.5181"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.get_statistics(fixed_seed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "uuid": "97aebfec-ec91-4d6b-af5f-ff36c5300122"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_value    27.489253\n",
       "pos_delta     1.271000\n",
       "pos_vega     73.308100\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.get_statistics(fixed_seed=False)[['pos_value', 'pos_delta', 'pos_vega']].sum()\n",
    "  # aggregate over all positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "uuid": "2448b331-3d97-4358-a07b-453dc15659f8"
   },
   "outputs": [],
   "source": [
    "# portfolio.get_positions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "uuid": "aa0c765f-2e15-4e74-a46e-ed8e7c43e825"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.450573"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.valuation_objects['am_put_pos'].present_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "uuid": "ea398cfa-fb40-4ed8-b9bf-c067d135fe11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6498"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.valuation_objects['eur_call_pos'].delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "uuid": "f8a828ff-4fc9-40b4-8dee-f3b27035978b"
   },
   "outputs": [],
   "source": [
    "path_no = 777\n",
    "path_gbm = portfolio.underlying_objects['gbm'].get_instrument_values()[\n",
    "                                                            :, path_no]\n",
    "path_jd = portfolio.underlying_objects['jd'].get_instrument_values()[\n",
    "                                                            :, path_no]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "uuid": "01f6d3d3-3459-4d94-abfc-ac6fbf887671"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "uuid": "9cd814c6-2762-489a-b926-883a078f0142"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LX/bJJ5o41q2bJo7VqJE4ubswZIgq8Ndf2jfnxhu1g9i770JOjq4zbBh06VIq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\n58sv6hjs2KEG5PLLk59O2a0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GYRhG6cBiVIZhGIbvWIzKMAzDKLWkraGyGFX6yg/gtx5+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\nqyESqggRIkSI0KshEqoIESJEiNCr4b/BQehJA1CHYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e41b4ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "plt.plot(portfolio.time_grid, path_gbm, 'r', label='gbm')\n",
    "plt.plot(portfolio.time_grid, path_jd, 'b', label='jd')\n",
    "plt.xticks(rotation=30)\n",
    "plt.legend(loc=0); plt.grid(True)\n",
    "# tag: dx_portfolio_1\n",
    "# title: Non-correlated risk factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "uuid": "873e0a44-470b-4fc6-ab12-956de039b21e"
   },
   "outputs": [],
   "source": [
    "correlations = [['gbm', 'jd', 0.9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "uuid": "3f517f85-6cd4-431f-9a86-51956d712576"
   },
   "outputs": [],
   "source": [
    "port_corr = derivatives_portfolio(\n",
    "                name='portfolio',\n",
    "                positions=positions,\n",
    "                val_env=val_env,\n",
    "                assets=underlyings,\n",
    "                correlations=correlations,\n",
    "                fixed_seed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "uuid": "be0fe6c4-3f67-4091-aabb-110043f3b2b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>quant.</th>\n",
       "      <th>value</th>\n",
       "      <th>curr.</th>\n",
       "      <th>pos_value</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>pos_vega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eur_call_pos</td>\n",
       "      <td>5</td>\n",
       "      <td>2.804464</td>\n",
       "      <td>EUR</td>\n",
       "      <td>14.022320</td>\n",
       "      <td>3.3760</td>\n",
       "      <td>42.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>am_put_pos</td>\n",
       "      <td>3</td>\n",
       "      <td>4.458565</td>\n",
       "      <td>EUR</td>\n",
       "      <td>13.375695</td>\n",
       "      <td>-2.0313</td>\n",
       "      <td>30.1416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  quant.     value curr.  pos_value  pos_delta  pos_vega\n",
       "0  eur_call_pos       5  2.804464   EUR  14.022320     3.3760   42.3500\n",
       "1    am_put_pos       3  4.458565   EUR  13.375695    -2.0313   30.1416"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_corr.get_statistics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "uuid": "c3f911fc-b3f5-4fd2-bf53-d45cbb78e67b"
   },
   "outputs": [],
   "source": [
    "path_gbm = port_corr.underlying_objects['gbm'].\\\n",
    "            get_instrument_values()[:, path_no]\n",
    "path_jd = port_corr.underlying_objects['jd'].\\\n",
    "            get_instrument_values()[:, path_no]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "uuid": "e2b1e218-8908-42e9-a9c7-8dc0c58a95c1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V1app9veLL+ryl1+q8br44tjJiIi1a9UnecYZPitiREKkkbUzgQ7OuYuAQ4Dy\nzrlRInJtYAfn3DBgcm4NdOvWjVq1agFQoUIFGjVqRFpaGrDfVxrP5cWLF3P33XcnTF5gOdgPnMjv\nG7w8aNCghJ/vZJG/ZAl06pTO+edrYlTLlmkJ//7Z74G89v/kk3SvAGwap50GVaqkc/zxcN11adSs\nuX8+p3DlV6mSBqSzapVO5hqr79e8eTo9esADD6Tx/PNw0UXpzJnj8/0wdSpprVpB8eJJ9TwIXg6s\nSxX5gc8ZGRnkiYhE9QecA0z2PlcPWn8PMDaXY8RvZs2alVJyk0kHP+XfcYfI/feLNGsmctVV/ukh\nkv/zkJ4ucuqpImvXinz4ochjj4m0by9SvbpI5coiF1wgMnly+PIHDRJp2zZ/OoRL584id94pUqmS\nyLZtofdNyP1w+eUi//ufvzrkgd86+C3fsws52pmox1E559KAe0Wkg3NuNNAQzf77FbhFRNblcIxE\nK9cwwmXXLjjySC0JVLq01iPt1St28Zl4cc89UKmSjm/Kzpo1MGWKppZ//3147bZrB9deC506xUbP\nYL75Bk49VSsVZU+uSDh792rA7Pvv4fDDfVbGyA2bj8ow0HJAY8dqdhvATz9p/brXX9eHdjIiogNp\nJ03SFPKcyMrSrLqZM+GEE/LX7t69cNhhOpv5YYfFTt9gnnxSC9DWqBGf9vPNZ5+pxVywwGdFjFBY\nUdocCPaTpoLcZNLBL/mvv66VFgKsXp3OBx/A9ddr5ZlEk5/zsGSJVrtp0CD3fYoU0Xp7EybkX/ZX\nX8Gxx8KyZXnrECkPPpg/IxX3+2Hq1DzT0v3+TSSDDn7LD0XKGiojtVixAn78Edq3P3B906Y6EeDF\nFx88UDUZ+OADNUJ5Fbnu2DE8QxXrbL+kJhXHTxUyzPVnpAS9emn1mAEDct7+8suaUv3551qzNFk4\n5RTV6+yzQ++3d6+GX779VuNweXHWWVrLr1WrmKiZvKxapcGyP//0eW4RIy/M9WekNLt36zx53bvn\nvs/tt2vPpUMHTbpIBjIy4I8/NOkjL4oX1zjbBx/kve+2bepSbN48ahWTn6lTdZ4SM1IFmpQ1VBaj\nSh35EydqjOfYY0Pr0b+/TtVTr57GrUaMUHdhfjv/IuENys3rPEycqK7K/D5j8+v+++wzdXmWKuX/\nvQBx1iGfbr9Cfx4KgPxQFMxSuoYRBtmTKHKjSBHNDFy+HObM0ThO377qVmveXN1vp58Of/8Nv/+u\nXqXg/6tElaPPAAAgAElEQVRXQ82a8NJLsYn/TJigyWr5pXVrzbLbuDG0+3LGjBSJT+3ercUMhw/3\nWxMjSixGZRRqfvlFew+rVun0FZHw229quObMgYULoUIFLcB99NH6F/h81FE6yWrPnlpW7rnnIp/W\nYsMGOOYYDa2UKpX/4y69VBNDrrsu933q11dXaOPGkelWYJg5Ex59VAOPRtITKkZlPSqjUDN8OFxz\nTeRGCrSXVLOmtpMX7dtrb+Wpp7Rw9f33w733hl+8+sMPtZ1wjBSo++/993M3VH/8obX3cprcsNBh\n2X6FBotRpYjcZNIhUfIzM+F//8vd7RcvPUqV0oy6r7+GefN0oO706eHJD6Slh0u7durt2rEj5+2f\nfKJTzAfiXn7fC3HVIQxDVajPQwGRH4qUNVRG4WfKFJ3+vH59f+QfcwxMngzPPquz5V5xRf4yCnfu\nVBdiJNUyKlbUONq0aTlvT5n41M8/w9at2q01CjwWozIKLe3aweWXQ7dufmuiBuqqq6BuXXULhuKD\nD2DwYA2xRMIrr8AXX2hiSDAiOsHt3LlqRAs1gwdrUccRI/zWxMgnNo7KiDn//AOvvQZt2+q4nGRj\n9WqNoev0GP5TqpSerzfe0OdnKCJ1+wW4+GLtTe7de+D65cs1VlenTuRtFxgsPlWoSFlDZTGqyNi2\nTXsEtWvrw9A5+L//S5z8/DJiBHTpAmXK+KtHMNWq6azgN96oRiQn+ZmZmkgRzUSDNWpozy178zNm\naCWK4HJMBf1+zJGdO7XbGEbZjUJ5HgqY/FCkrKEywmP9enjoIX0bX7JEYyCTJ+sYpeHDYeVKvzXc\nz759qlN+xk4lmmuvVYP12GM5b587VwcdR5rWHiCnwb8pU98vPV3LJh16qN+aGDHCYlRGSNau1eka\n3nxTpxi///6DXUdPP63VDj780B8dd+xQQ7punf4tXKien2Sd1WH9en2ODh+u1X2CuftunXbj4Yej\nk7FyJaSlqQu0SBHtwVWurOPK4jWtR9Jw++06nuA///FbEyMMbByVERE7d2q1g7Q0+O47qF495/16\n9tQK5IkKC8yeDY8/Dr/+qoZp3z7tpVStqv+rVYMXXoi/HpFStep+w79gwf4isiIan4qFwa9bVzMA\nv/4amjXbP61HoTZSWVladfj9922QbyEjZV1/FqPKmzvugIYNtXp3bkYKdDDroEHaG9izJ3bys7Nu\nnbrOrr5aa/FNm6Yz3O7cqQVcv/5a3ZHDhml18HjpEQvOOQfatk3nyis1LgVa+bxYMTjxxNjICHb/\nBeJT2SlI92NINmzQzJ4pU3RysTAzRgrNeSjA8kORsobKCM0bb2iK82uv5T0XEsCFF+pbfDx6Mvv2\nabZxgwY6lcX336uxOvZYKF8+f/olI1dfrckegSnm8zv3VH4JGCqRQh6fmjdPfaknnaTxqWgDfEby\nISIJ/1OxRrKydKnIYYeJLFsW3nErV4pUriyyZk3sdPniC5FTThE555zw9SkIrF8vcuSRIlOmiDRs\nKDJnTuzazsoSOfpokc8/FylTRmTnzti1nRTs2ycycKBI1aoikyb5rY0RJZ5dyNFmWDKFcQDbt0OT\nJtC7d+jCprnxwAPqonvjjej02LRJ25oyRZM1rrqq4Pac8mLuXO1JFSmiySuxnDqpZ0+NT5Upo+WT\nCg2bNukN+tdf8M47mjxhFGhswG8OWIzqYETg1lt1or5IjBRottqMGfpwDFd+gMWLtfp4yZL73Xzx\nMFJ+X4uA/ObNtcj3TTfFfn6/jh31WuQ2pMjvcxC2DuvWwZAh6uo79ljNrImBkSpw56EQyg+FZf0Z\n/zJsmI6R+vLLyNsoV04nILzzTm2nSJivQu+8o0kcgwdrVlzCWbtW/04+WTMbEsRdd8Wn3ebNNQuy\ndev4tJ8QVq+G8eM1m+/bbzUg+vrrYQ3oNQo25vozAO3FtGqlbqjjj4+urawszbq75Zb819nbt0+T\nCsaO1aSChNcSzcjQkhvvvKMpjqtWqQ+0eXP9Ms2aaeZGAWTr1gI49nX1anj7bTVOP/wAHTrAZZfp\nTRrNnC1G0hLK9WeGymDbNnW1Pf44XHllbNqcP1/LAK1YkffzfetWjUHt2AHjxkGVKrHRIV+sXKld\nwEmT1O95992qwObNmvY4d65mlS1cCMcdp3nljz4KlSolUMkUY8sWqFdvf1Xhli3Dn9DLKHBYjCoH\nLEaliGhspFWr2Bkp0M5I+/baM+reXWeUzchQecHyV6zQaSnq1NHYVsKM1NKlpJ97rvaW6tSBn37S\nooUBBSpW1NHLTz6pZTc2bYJXX9USD02bquJREta9sHt31PJy1GHqVN/rXx10Hh57TI3U669r6Y4E\nGKlk+12movxQWIwqxQnEpH74IfZtv/oqLFum8e4PP9SKNiVK6HirH36A0qXhvvu0Q3PjjbGXnyMi\nmp3x6aeaajdxogbW8qJECXX/NWumszG2aAFjxiQm+PP332pMmzWD//5XR2HHigkT9HuMHw9t2sSu\n3UhZtkz9v99957cmRhJhrr8U56GHtDJCXnMkxQIR7bjMnq1/K1fCc8/BGWfEX/a/pKdDjx5au6h0\n6cjbmTMHOnWCPn00cySeufPPPKNuyBYt1Kqfe676aY89Nvq2zzpLje0rr8DLL6urzS9E4Lzz4NJL\nNaPGSClCuf5swG8Kk5UlcswxIgsW+K1JAunSReSFF2LT1i+/iDRoIHLzzSJ79sSmzez884/IEUeI\nLFqky9u2iTz+uI6svuUWkT/+iLzt9etFypdXGYsXi1SvLjJ8eGz0joR33xU56SSRvXv908HwDUIM\n+LUYVYrIzUmHb77Rl9hTT/VHfsL56y+YOhW6do2NHrVra/HTNWu0V7JxY1iH50v+qFGaKn/KKbpc\nrpymR/7wg35u0EB9qps2ha//1Kmkn3yyDlhr2FB7m/36aeHGBJKenq6ZNPfdBy+9lNBhAQfo4DN+\n6+C3/FCkrKEy4N13daxSYa34cBCjRmkqYsWKsWuzXDnNp2/SRJMspkzRsT5//KHTIEfDvn0602Lv\n3gdvq1xZS3YsXaqG8tprw29/8mQd3R2gbl11ab76qiY0JNI937+/uiHPOSdxMo0Cg8WoUhQROOYY\njaEnfMySH4jACSfo1L/5Ka0eCWPGaPsbNuz/K1lS59Y47DA44gh4/vn8V/Z+913t3cybF/ptYscO\nndb3hx90dG9+2LNH5xvJ6Zh16zTbLi1Ng4jhjtoOl59/ViO/ePH+OU+MlMPS042DWLhQPSyxTCBL\naj77DIoXP7AHEWuuuUazCZcs0V7O7t3as/rkE01UOOMMHRP08895tyWivYw+ffLu8pYpowNi33kn\n/7rOnq2GOyfDVq2augHnz4cbblBjFpiLJB7ccw/06mVGysiVlDVUqR6jeucdTVrL0+03aZJmYL3x\nhqYM79sXE/kJZ+hQuPnmA75w3PVwTkc716mjg8V699Y0y5YtYdWq0PKnT9cxW23b5k/WNddojy6/\nTJ4M7dvnrkOFCqqDc1qyqFw5fau5+modWzZxoqZwRns/TJ1K+qJFaqx8JFl+l6ksPxQpa6hSGRH1\nKnXqlMeOq1frAKfDD9eH1iWX6AOsRQu491546y347beE6BwVGzbo9MNeEoWv3HyzjoC+9dbQMaD+\n/dWw5dftdu65WvYpPwPiRP41VCEpU0bHjP3yi57D4cM1aWTzZi0Me+650Lhx2Ekk/7J7t5Z3v+MO\ndZEaRi5YjCoF+eorrcG3fHmIHpWIlt5u1EgD6wE2b4ZFi3Qc0vz5MGuW1j0699wEaB4hzz6r7riR\nI/3WRNmzR2tWPfQQdOly8PYvvtAyIT/+qO7K/HLvvVC2rI6xCsXy5dpLysiILpNGRI3pjBnq3gw3\nSWXAAI2/TZ4cuQ5GocHGURkHcO+9Io8+msdO770nUq+ejrEJxaefilSrJvLTTzHTL6ZkZYnUrSsy\nd67fmhzIF1+IHH64yMaNB2/r0EFk8ODw21y4UKR2bf3OoRgwQKRHj/Dbz4msLJG77xZp0kRky5b8\nH7dypUilSiI//hgbPYwCD/EaR+WcK+qc+8Y5N9lbruScm+GcW+mcm+6cqxBN+/EkVWNUWVkwalR6\naLffli0678TQoXm7ZFq2hL591Y20bVu+dEjoOQiRROHrtWjWjPRmzeD++w9cv2yZdnmvvz78Nk85\nRSuLf/556P2C3H5RnwPnNDOwSROtjfj336H3F9H76swztRzKscf6/psA/3+XyaCD3/JDEW2Mqiew\nHAj48XoDM0SkLvCJt2wkEV9+qaGHE08MsVPv3ppF1rx5/hq97TZNZb7qqpgkW8SUHJIokoabblK3\n2axZ+9cNHKgvCZGUd3Iu76SKDRt07FVaWvjth5L70ktQv74Wk92xI+f91qzR5JAhQzSrsHv32Olg\nFG5y62rl9QccCcwEWgKTvXUrgGre58OBFbkcm5CupHEwPXuKPPZYiB1mzxapUSM8N46IlhBq2VLk\nP/+JSr+Y8tdfIoceKrJpk9+a5M6kSSLHHiuyc6fIr7+qO2zz5sjby8jQ8kq7d+e8fdQokY4dI28/\nFPv2iVx3nch55+n3CZCVJTJ2rEjVqiJ9+8av3JRRoCFOrr/ngfuBrKB11URknfd5HZDP0YdGIsjK\n0ryHXN1+u3dr7+PFF8Ofaa94cW38vfdg9OiodY0JI0fGvhJFrGnfXl12TzyhSR/du2tmZaTUrKnd\n5alTc94+ebL2euJBkSKaGVitmibi/POP9uA6d9bvN2WKJuaEkyBiGEQ4zYdzrh2wXkS+cc6l5bSP\niIhzLtfUvm7dulGrVi0AKlSoQKNGjUjz3BEBX2k8lxcvXszdd9+dMHmB5WA/cCK/L0DRomlUrgzT\npg1i3boczvesWXDCCaRXqgTp6ZHJmzSJ9DPPhL//Jq1Hjxz3HzRoUPyvtwhpQ4fCiBG57h9Yl8jr\nn6P8zp3hhhtIK1IEli+Pvv0mTeC550i7+OIDt595JsyYQXqXLv9e35jfj0WLkn7DDfD446S1aQM/\n/kj6WWfB88+T1rhxjscn5H7IY9mv50HwcmBdqsgPfM7IyCBPcutqhfoDngRWAb8Ca4EdwGjU9Xe4\nt091ktj1N2vWrJSSKyJyxx0iTzyRiw7LlokcdpjI6tXRC5o0Sd2HubSVkHMwa5bIiSeGzIDz81oc\nJP+tt0T69YtNw5s2aVX07C7EmTNFmjbNXYdYsmePyP33i6Sn57mr39fBdEgO+YRw/UU9jso5dw7Q\nS0TaO+cGAhtF5CnnXG+ggogclFBh46gSz759WqEmPR2OPz7bxqwsOPtsrTrg9YKiZsAAeP99LdVT\nqlRs2gyHq67SiQbvuivxspOByy7TLLzgGSnvvltrDj78sH96GUYuJKLWX8DqDABaOedWAud6y0YS\nMHeuhg4OMlKgWVig1RJixQMPaBZY3bpaeWDGDB3omgiSqRKFX1xzzYGxwvxWozCMJCRqQyUin4lI\nB+/zJhE5X0TqikhrEdkSvYrxIdhPmgpyA1N6HKTD1q06v9HQobGtku2c1gecNk27co8+qpayc2fS\nH35YK1zEmsxMnfO+UycN5ueRROHXtUiI/Isu0jT033/X5RUrtHbgyScnTod8Yjokhw5+yw9F4mco\nMxJOZqYm482bl8PGceN0DqCQA6sixDntVdWvr2Oz/vxTDcmIEZpZ2LAhVK+uBU/Ll9//F1g+8kjd\np1y50HJ+/13bHD5cj7npppxLE6USJUvqtPJjx+q5D/SmknE8mWHkgdX6SwE+/VQLICxcmMPGFi10\nZlUvQyxh7NypFRQ2btRqBtu26V/w519/1YrtNWpoCnfgr1EjnThwyhR4/XWtjXfVVWqgsvUYUpo5\nc9Sdu2yZvoz06aM1/gwjCQkVozJDVcgR0Rfpli3VHh3Ar7/q9BN//AElSviiX55kZmpF8G++2f+3\neLGO0Tn1VDVOV1wRWSWHwk5Wlk4xMnw4XHqpToh4yCF+a2UYOWITJ+ZAqsSohg6FtWvhzjtz0GHM\nGA1cJdhIhXUOihVTt+Q11+iA2E8/1V7YH39ohsh110VspPz2ycddfpEiet5uvlmr2+dgpPw+B6ZD\n8ujgt/xQpKyhSgVWrNBM5DffzMEWicCoUXDttb7oFhXOJXe1iWTi6qt1Pql4VaMwjARgrr9wGTsW\nGjRI+ljInj06jOiWW/TvIL74QielWrHCAuyFnYce0rmqKlf2WxPDyBWLUcWKjAyoV08D98OH+61N\nSB54QG3QBx/kYod69NAkhYceSrhuhmEY2bEYVQ5E5I/t1Ut9/pMmaZA/UXLD5NNPNfw0bFjORip9\n+nQdWHXNNXHXJSeSxRfutx5+yzcdTIdkkh+KlDVUYfPJJ+xesJTL/nqNt8vdpKm/ScimTZpfMGIE\nVKmSy05ffqnuy5o1E6qbYRhGJJjrLz9kZrK3YWMuL/MRa+QIdv3+F0uveBw3+CW/NTsAEc3UPuoo\neP75EDt27Kg56zfckDDdDMMwQmGuvyjZ9/JrXLNhEFnVqjNvHhSpUI6Zb2/QcSpJxBtvwMqV0L9/\niJ02bNAZZS+/PFFqGYZhREXKGqr8+mOz1v3FjQ8cxqY6jRk3zlGiBNz9wCEM+udWWLAgbnLD5aef\n4D//0aTEkGM633mH9NNO0xJFPpEsvnC/9fBbvulgOiST/FCkrKHKDyJwx7nL+blyEz6YWfZfA3DV\nVbCA0/jh9dn+Kuixa5cOl3n0UQ09hWT0aGjdOiF6GYZhxAKLUeWCCPS6dj1z3v6DmT/VonzNAweY\nPnrTWjaO+4SXN1/t6zikrVuhQweNS40enYcqP/wAaWmwapVWfDAMw0gSUi9GtWMHjByp1iZC+j4q\nzBy/jY8HLjnISAHc1u9w3vq7HZs//z4aTaNi/Xqt4deggRaZyNNejh6t3UEzUoZhFCAKp6EaMUJn\nNu3RI9eEh1D+2AEDYNyIbcyocwuV7sp5rFH1Ixztjv+RYf3+CEu1WPmBf/tNJ+Vt2xYGD87HVFJZ\nWTq4qmtX333RfssP4Lcefss3HUyHZJIfisJnqLKy4KWXdP6dZcu0uva+ffk+fPJkeO3VLGbK+VR9\n7XEoWjTXfXveV4yX0htEOvY3Yr7/Xo3UbbfBE0/k0/M4Z44mUDRsGHf9DMMwYknhi1F99JHOWLtg\ngboAO3TQyfRGjMjT5bVli7rRxrQcTlrWp1rNNRT79tGi1Nfc+Wxtrrjz8Bh+idxZsEDriz71lA7s\nzTc33qjln3r1iptuhmEYkZJatf7atNE4TKAq+M6dcMklUKmSxmiKF8/10O7d9lJ85XJeXXmeznl0\n5JF5ihvf+jWeXdmeeRk1YvUNcmXWLJ1lfdiwMOc53LVL6/otWwZHHBE3/QzDMCIldZIpVqxQA9O5\n8/51pUtrbb6//9bpyffsAbL5Y7dsYcZ1Y5gx+k+equrNeZQPIwVw8d21WfOn4+uv86diuH7g7dt1\n2qUBA9RIvftuBJPxTpwITZr8a6T89kX7LT+A33r4Ld90MB2SSX4oCpeheuklnSSuZMkD1x9yCIwf\nr7Gqyy+H3bt1/fr10KcP2+uczM3jL2DIa1D+g1FhTeFR9PyW3FnkFQb13xm1+tu3ayhp0CDo2hXq\n14eqVXWGhowMmD5ds/zCYscOHWB1xx1R62cYhuEHhcf1t2WLTrv93XfsKF+dSZO0Y3VANtzevToy\ndts2OP54dQV26cKdOwbwtyvPG29EJnpr55up/eGLLPnhkPx2xADN+/jmGw2rTZ0KS5boZLannbb/\n78QTQ3or8+aOO3Sw1ejRUTRiGIYRX1IjRvXcc7BwIbz5Jr17w8svQ4sW+nyuVClov8xMrTVUpAjc\ndx9zfqpOly6wdGm2/cJhwgTuugvKdu3Ik0+G3nXTJu0ZTZ0KH3+sE9VeeCFcdJFm8oUsfxQuM2Zo\n4dmlS6FChRg2bBiGEVtCGSpEJOF/KjaGZGaK1K4t8uWXsny5yGGHifz+u8g99+jqRYsOPmTWrFmy\nc6fIcceJTJgQpfwdO2RlmUZyWOV9smPHwap98YXIY4+JnHGGSKlSs6RdO5GXXxb55Zco5YZi82aR\no44SmT79oE2zZs2Ko+C88Vt+AL/18Fu+6WA6JJN8zy7kaDP8LVEwZozOiXT22dG18+GHULUqcnpT\nbj9Ps9OPOko7WU2bamm7gQPh+usPPKxvXzjlFE0KjIrSpTnugjqc8ctqxow5mrZtYdo0/Zs5U3MY\nLrgAHn9c3X0JKbV31106lUerVgkQZhiGET/8c/39/rsOPi1ZUgflPvpo5KV9zjsPbryRt9xVDBwI\n8+cf2NTy5XDppXDOOfDCC+pemz9fn+NLlmjCQtS8+Safvvw9Fy78L2XLwvnna6Z869aaGZ5Qxo/X\nuegXL4YyZRIs3DAMI3ySMkaV1f0mbvjqZio0O4EHV15Plb1rdI6KcGedXbYMWrdm25IM6jUswXvv\nwRlnHLzbtm0arvntNxXTsSM89BBceWVsvhNbtkDNmvwwaw3HNiwTqqBFfFm3Tl8AJkzI+UQYhmEk\nIUk5jmrQ2Kp8X6IRmSXLcsLSd+lXZiB/n5amA4XC4cUX4bbb6Pt/JWjTJvdnc/nyMG6cDqU6+WQo\nXz6dLl2i/hr7qVABzjyT43/+KKSRiutYBRG45Ra1yCGMlN/jJfyWH8BvPfyWbzqYDskkPxS+Gar+\nrg9vjSvGSy/B/PmOn6qdxXGykhdv/4Hd19+q43/yYuNGGDeOJc178OabOig2FM7BfffB55+rZyzm\ns3NceqkaWr+mMBk5En79VYNvhmEYhQTfXH/vjtrJFV1LHbB+yRJ48D+ZLJu7hcfLPc3V4y+jaLMm\nuVuUp54ia/kKWvz8P7p21c6Er2zYAGedBf/8ozUG27fXwFj2Acjx4LfftPrEzJlhDVg2DMNIBpIy\nRhVK7pw50Kf7ejb/spnHKr7IZR2zKHJBK02aOPRQ3SkzE+rU4Y0bZvPylFp8+WXIQueJQ0SzNyZN\n0lLsy5dr5l2HDjpYqnJl3eeff7TXuH37/v+lSkVmZHbv1sFYrVtD796x/06GYRhxpkCOo8rKEpny\nYZY0PnGnnFR9vbzf6HHZV6acyFlnifTrJ9K/v2xq1kaqVROZPz/8nP2EjRlYt05kxAiRjh1FypeX\nWaVKiRQpInLIISKVK4vUrClSv75IkyYiNWqIXHutyKZN+W//m29ETjpJ5PLLddBWPvB7vITf8gP4\nrYff8k0H0yGZ5BNiHFXS1vpzDi5q6/h6aSmeHFqF/7pHOO2YLUxsNRjZug3eeYeHKr1Gx47QuLHf\n2oagalUdwDV+vNYWHDdOe0C7dqmrMCMDvvsOvv5ap4ovXx5OOgmmTAndbmYmPPmk9tZ69dLYWFJ0\nKQ3DMGJLUrr+ckJEvWl9++rz+NproX9/9axFXPooWUlP18y9Fi20Qm328kcrV+pkVGXK6DxbRx/t\ni5qGYRixIinT08PFOZ3eYtEiHf80Zgw8+2whNFIAaWmaWVK2rPauPvpI12dl6bzzZ56pc25Nn25G\nyjCMQk9Ehso5d4hz7ivn3GLn3HLnXH9v/WPOudXOuW+8vzaxVVdryV56qVaWuPrqyNvxa8xAvuWW\nLatGadQouP126NZN6zCNGaP59Xfema00fBx0iBN+yw/gtx5+yzcdTIdkkh+KiJ50IvIP0FJEGgEn\nAy2dc80BAZ4TkVO8v49jqGtMWbx4ccGQ27KlVj8/7DDNepw7F+rWTawOMcZv+QH81sNv+aaD6ZBM\n8kMRcVFaEQnMFFgCKAps9pZjPYw2LmzZsqXgyC1bFp55xl8dYojf8gP4rYff8k0H0yGZ5Ici4hiV\nc66Ic24xsA6YJSLfeZvudM5965wb7pyzSZAMwzCMqIjYUIlIluf6OxJo4ZxLA14FagONgLXAs7FQ\nMh5kZGSklNxk0sFv+QH81sNv+aaD6ZBM8kMRk/R059wjwC4ReSZoXS1gsoiclMP+PhXDMwzDMJKV\n3NLTI4pROecOAzJFZItzrhTQCujnnDtcRP70dusILA1HGcMwDMPITqTJFNWBkc65Iqj7cLSIfOKc\nG+Wca4Rm//0K+F0m1jAMwyjg+FKZwjAMwzDyS4GpTJFsOOeqev9Tzo2ZDN/ZOXeSc+5Qn3VI+Hlw\nzlV0zkU8rKQw4pw7zDlX32cdivspP4DfesRLfoExVM659s65e5PgQpzonPsAeBC8cr+Jk93JOXdX\nouTlosMNQJqP8qs45wYDI4FjfZB/iXNutHOuVIKvfRnn3MvA04CvBjqAZyBedc5d5qMO9wCfAmf6\nJP8k59yrQEM/5Afp0dE59w7QpDDKT3pD5Zyr75ybDNwDzBeRvT7q8gowDpgkIncnUO4JzrnxwB3A\n94mSm02HS51zc4HWwHd57R8nHU5Cx+2tAU4XkYUJlF3POTcVuAkYKyK7EtWjcs61Q8/5bqCniGz0\n1vvWs3XOPQ5MBLYBH/ggv7RzLgM4FzhfRIYlWH4J59wLwChguYgsSKT8ID0Od859BtwGDBeRzwuj\n/KR2IXgZhT2Bo0Wkobfu39LrwZ8TRFkgQ0RGePKrAX+JSFa8BHoPo+eBrSLSwltXVET2xUtmDjqc\nAvQDxouIb/Pci8hS7+E0X0QynXMtgD9E5OcEiG8LtBaRogDOuWIikpkAuQDHA+lAHxHZ7ZyrJSIZ\nCb73/8VzPT4MdBWRN711Cbsnvd/9TudcOrBPRNY7505Eq+JkiMj2BKhxHnAq0FdEJiVAXm7UACoC\nt4rI5865MiKyo7DJT/pkCudcU7QnMQhoABwF/CQibydA9snAOhFZ5y2XAFYBLwAtgY3oW+4DQWn5\nsZJdUkR2e58vAy4F7gOuAA4H5gCLRGR9LOUGyQ9+ITgM6AoUB0YD3YCf0YfC1865IvEw1s65hkA9\n4CMR2eatawV8CExBfyAO+BJ4SUT+iLH8Q0Vka9DySqA3+sJyNprZ+pKI/B1juTVRr/Lv3nJd4Dr0\nu1z+IWsAAB7dSURBVJ4AlEZ71vNE5L14nf9sOh0KbAeyREQ8l9slwLXAk8BPwFwRmREn+eXQe39m\n0HkpAawH5gFVgV+A8sC9IhJzz4NzroqI/OV9rgDchT4DtgEXAEuAH0VkQqxlZ9Ojuois9T6XAa5C\n78dFQDtgGfCViLxVaOTnNqOiH3/ARcCPwBlB68oBfYB9wPvoA/NH4I7AczQOelRA3RqZwA1AqaBt\nPYENQBv0gTUWeAAoGSPZ7YBPgLuyrX8bfRiMA+4E3gP6A0Xi8P0fBNKzrTsDdfFsAl5Be1ir0d5u\nPO6FrkAW8DmQlm3bSOAR73M9YAxwYQxlt/LOdW/g0KD1HT2d3gHOAmYATwBHxEiu887rbuCTHM5H\nOnA3Wg3mevTBWCEe5z9I7iHAm8Bs4LRs21ajrtibvHtyOtAmDjqcBvwO/AVck+33eLN3PUp4yy8B\n/4mx/JrANGAFUDpo/Xne+m+ALt6zYSFwVpyuRTPvfC/Ntv449MV1BtDYO0e/AocXFvlxu8EjOAlN\ngHeBuWhFi+BtdYJ/AKj1/iOOupzk3XT3Ac8Bp2TbfljQ53OBr4FiMZBbB/jCexC/DDTMdjNcHrTc\n0tuvdgy/dxE0FviR9xDqE7TtEODibDoNBv4Xh/NfAk3YOA141PurEbS9WLb9hwP3xUh2dbT3Ps77\nfmdl29462zVZCNSKkezyqJv3TGAq6loLbCsbLActBD0uXg9FT0ZxtBczDjVWtwEVg7bXBY4LWn4J\n+L846HEycA7ae3sp++8x276XAR/EWP7/oV6UkcDAoPXO+/0XD1r3RJzOQWngfuBG9OXt+qBtxYC6\n2fafHPz7LfDyY31Cw/zyDjjE+3wYUN/7/C1wVdB+RbMddxT6Fl02hrqcFyS/hHdhSgKvo2+LFXM5\n7lbvx1MiQrlFsi0fg9ZL7Af0C3HcyWgPs2gkcrO1VTKgB3CK91A8AdgClAveL9txd6K+6Vic/wvQ\nHsxxgWsQ9D3HoEbyoO+KGuy5QIsoZBfF6xV55+Jo7/PzaO8y1zdD7wdZJwrZTVGDV9Zbru79vwxY\ngGeUyeY5QHv0Uwnq8cXqj6BeMnAE+gLTCk0cOCfEcb2BXjGQXxd4yLu2Rdgfogj5e/R+OxPQl8yo\nPC3oC0vg3B+FZlrWQ6vt1AvaL/vvdwQx6lXiGQC8XlzgPkM9T8uz/TZd0OcS3rU6oyDLP0CXWDUU\nwUnoiVrm4RxsjS9DjdUhwTeE90C5Ae1qPxwjPY4CFgOzgJlAd4LcKcCFwBvejybwgymNun4+Bz4G\nToxQ9k3edxkAXJpt24XAELw3ePYbkhKou2Ox92Bwkf4ovfP5OtqT7Re0PvA93wLGeJ+D3xrLoW+O\nS4jCQAS11xf4Ae29vg/0yLb9ftRoNAjSu7r3Y/gKuCQK2bd553IKcDlQKWhbEzQm1zbw/dFeRjHg\ndrQ39TQR9KaBUmivOcP7DUzK4dq8DTyR7Zqc6H3vBUDH4G0x+i1MR918A4GTs20fiPZujwrck56e\n9VH324LANYpCh1bAn8Az3m/rQQ70YAR+j+cFrTsUuBd110bl9kMTJL5FX0BGEvQM8rY/DowLfP+g\ne+IC79xNIKj3H4Uel6Lxt4nAeLIZZtQNPyDoOjg0ZHEj+kx5LbvuBUn+QfrEqqEwT0Jj1Cgc6934\no4GLsu0zDXgsaLmo93CYRDZfeZS6tAKe8T6f7z14nsi2zzPAg97nkt7/tkDnKOQ28X7YTVHD/CUH\nujerAL2AF7Md1x2NVUR1Dryb62Hvx3g08BnwCN4bvbdPeWBrsCygGhqXe52gh3qEOjjUpTgEqBl0\nPd7kQDdnDfTh1B6oBJzgrU/L3l6Y8iuhbs4G6ANwEEGuHW+fh73rXyVoXXM0RhjxNfDu/U+Clj9D\nH7bBL2dN0Tf4QO+yJBorvCdW9382ne717v/SwH+B/2W79g29a9MhaF1pvJ5njHS4B7gu6DfyFPBk\ntn2e9vYrDzQJOlfBLxlhx269+3EkcIu3/DY6I0RwXKoa6uoPvEAW99a9C3SJ0Tko4+nR1FsegXpY\nTgzapy4aBwr0wEuhLxpPBs5JQZWfo07xuOFz+fJFgj53AT4NujnuQ3sVwV3q49GxI2ehSQM1CXL1\noQ/aiBIJvBsr8OPvjefTRh+aZ6Bv102C9q+Kup+mAL8B1SKUWzToc1vgqaDlq9FsxuD9T0P94/d7\n5+cwYuDqC2p/DHCj97ke+sJwJfpADLzB34caxpOBO711wQ/tooRvIC7gwNjGPKC797ksGoydwIEP\n7UvQrKKteG9yQdvy3aPhwJ5hCzRTLXA/NUJ7dO2D9qmC9nzuRo3amdnv6/zehwR5DlA31dvsd3We\n7rXfOPC78P73834HX6Czakf0vfOp32S83inaY+0FvJFtn5u83+PTwOvZ9QhXJ9TANMJ7Y0cN09uB\na+Vt/zBwXrz1h6Pu3tWogQ9Orgj7fsymz3D291QroAkCHTnw+XWxJ/8JYvTSAJTPtvw10Nb7XN87\nL3dlO9cPoy/0Y9A0+QIrP6+/hAz4dc49CDzrnOvgrZoP/O6cayj6jaeh7pRmgWNE5Af0oTUDrdT+\nm3jjI7wxG1kSZkquc+4q59y3wIvoGxDojVnDOXeqiPyDpv3OQt1AARqjD/DNQHPx0tXDlN0PGOCc\na++t2oM+KAEQHY+ywTl3f9Bhy9HA+sPoQ3uDRDhWxTlXwzn3jHPuRi/tHvTBX8Yb+/A9mrlzBnCk\nd11A36pboA/RVZ6ufzmliIjsC9o3Lx3OdM59gmZxvuxVmAANVF/hnCvuXeN56KDeVt5xlYHHgF3A\nuSLSO7hdyed4Ju8ajPYGqyIis4ESzrn23r30Ixr36eS04DKi6cinob3N5RI0oDG/96FzrolzbgYw\nzDn3tDfkIjDWp5J3Hr9G3Z9Xe3LFaVmgdsAO4CERmRXUpsvv985Fp7Odc9Occ08G3ZOfoj12RNOP\npwAlnXMXBR36N/rycjr620F0TJsLRyfnXFXn3Ci0Z34P+jsHfSk40vs97kWvyWx0oHmgRM+D6Bt9\nLxE5R0R2BdoN837s6pyb4px73DkXePZsB4o7rTyyBX2Z6MqBxREOQ3+XJ6Pu8ahwzj0KfOqce8o5\n18VbPQFo4N0by1E3+1EcWI2lAprM8buI9Cuo8vNDXA2V9wP9Bv1yK4DbnXPd0DTT9WhvCRFZhk60\neIx33KHOuSfQB+mxIvJIcLvhPqydzkZ8DRqP6CEindGLcIP3IPoA6OHtvtXTRbzR54egN+YFInKN\niKwKU3ZT59xC9CIvAR53zp0vOt6klHPuzqDd/wNc5I0PAY0J7EV7mhFXwnDO3Yb2ijLRt6O+TmsV\nrkIzDY/3dn0HDexX945r5K0bKCJHisi/FQhEyfeLgnOuCvoQfktE0lBfdifnXA00FrEGfWMDTYEt\njRrzAHeLyFkistC7nvm+d51zxzrnvkR75U8CbZ1zT3mbh6MPIkQHKn6LGoajvWdvR0+fk0Wkl9ee\n8/bP8z50zp2D+uuHoX7/ncDF3stOBvq2XsXb/XmgozduDdT19YqInC4inwaMgSc7Xw/jHPQp5r04\nDkZjXSuAUU4H8Y4G9jnnLvF2X4+6Hqt5x1bzvsNtnoH4MlifMAxESfReWCciJ4vIdUBp59w1omOk\nPsK7F0RkEzobQ4BiwPsiUlW8sZQuzNqHzrlynpG8AXXrlgSud85VQt3x7QLfWUSGo8+v871jz0R7\nVC1F5GKJYvyk06oO73jtB4Yb9HQ6Zmwp2nM8x9v9M/SFKcs79hz0vNQWkQcLovywiGd3Db2gXYKW\nr8aLuaAPh+fw4jJottmX7A9QVg46rhhRjhdCe0VHBi13BUZ4n+ugb3QB91M7srk8opDbFLghaHkA\n8Jr3OQ01ihW85XpoBmEZbznqYCTqPnmM/YkINdC31uZoEPplNPZ3pLf9WbzECu+8B48jitjVhD4M\nTvE+F/X+j0F70Q59Q/0eL/0dDeJ2yKGdSBIX6uO5c4LutW89nWqgb833e9sORcexBa5JiaDjiub3\nPmS/664sB8Z0ugDveZ9rooHqbuxP1hgJVI3F985Fr9JAJ4Lc16hhuN77fC36UhPIeHsONUz/XrcY\n3Q8nc6BrtxeeGw0dJ5bOflfz4+SQ8h2l/LvZ726sj76s1vCW30ZfagPZn/8lRvGnbDqU48Ds5qro\ny0Nd1FA+iL6sVva2v89+d1zU4yf9lh/OX1x6VIG3LPQHPy1ouTr735JnoA+mAc65s9EbZzZeWSfZ\nX8+sqIhkSvQj75eKyOogXRp68hGRX1C/+23OuSHogNY5UcoL8B3wlnOuqLc8B31rLSYi6WgX+3nn\nXGfUJVZZvBIkoq7IiPG67XuBoahbCdHqDfX0o2xFf6B1gP5OSyU1Q12feOd9a6AHI2G4mrL3eESr\nbHzrfd7ntNLB6eh4OBF1qQ0DHnDO/YK6mNKztxuODkH8grq1AnqVBpaJyG7vfAxEe/vXoW7I7ey/\nD/d4xxUVdSvl5eYr5R0nnitsO+pCC7Aa7a2XEpHf0N5WY+A959xS9C11U1B7gR5LTMo1ichOdDD3\nOudccc+VtgnNfERERqFZd8O8nvi5aI8S8XqQbr9bNBqdvst2f5/nyUVEVqMPyfOdc/PQdOg3c/gu\nYcsPui+Hishm73e4HKiM9iBAQwN1gWeccw+hMdMcJ4GNBtGKJpODV6FjOLeI9rjfR5MU3vJ6gHXQ\nXg/RPg+9e9M3+WETw7eDXAOY7O8l3U+2QWCoT3wI+oMtHgM9Qga32f+m+BrZxjug45euIFu6fBiy\n80x0QF0uDwYtl0Z/iG8TYapzfnVAey5lUeMYnMFTCe1JTSHGGWU5XQu0ZxIojZR9WzkOTKoJN1Ej\nzzc9NMNvdHDb6AO5F2qoIroP0ReNR8mhSknQb+A/wKBs24qjMdC0WJ774Pshp/PI/l5fOkGp6N49\n0gnt3Z0XpfyQ9zP6QlAczQKu4q0LJDqVIluKfATyywd9zvFeQo3STA7sPR+K9qqeB46Pw3XJ6Xqc\nAHyYw/pL0Rf5iJ+P2Z8LiZYf9fmKxQnP/nDIYTnwg5iOlzXFgWmvwZlYEWW1oQMTg92FJUK1ifpc\n/7+9cw/aoq7i+Oe8lNwVBZTQFC1RCwcyY8xEQUiwtBrSQs0CjMwyK/OuJVlQYyWmKZVmA4mIUXQR\nL13MSzll/lHqVFr9UU1TU5OVjTpaw68/vufH/p5993l5nmf32ZepPTM77+4+++757e927ufs4Rtm\n6Qjq3Ka3kFwAMBmB/FZcfIh72TXf3rL4/XoWrR46cQymAw8n9w+K+Gn1bOrac4psMzY/zidzcc3P\niWORR9seSN3wtoJ51fFcKJqH7b4Dubqf7udzKdhMu8Qdx3aOb3gz27XDN76j0Qb9QQanJerqu3fU\nJj8fM8RzB6GckfhYHNZp3+4Af35jnJzMj/xvY1HIwzgU6LtmqO/pog0Tcdd2ZIc5KPd7HJOFuKrf\n94Ojyvb/EG1aiAfKJvjj3+PJPClPpIIYRX9Xuq4PLej/vuKv4iil+nN1SAghbDOVQXinmY0KObEw\nhBDcUPks8KyZ3QZ83Mwmugj6b7cTD4TeMzCvB95kZuPM7AZkIL7I8be80+RNNQHZbjYApVRsjiOY\n2V5mdjXirKclakaAba52+Acw3cw2I2++Xfz/nx/00g4gb1w3syPM7CZkC9k+vvF3tDH9xJ08HkDG\n+wHkWbnNzEb4mAS6hDjuPicCCk6Nnp75952E7ITfRkb7Tbl3hU7nQm4ezjCzj5pKgsRxMX8u9scI\n4D9mthERjim593U1D4OroEIIDyBj/DI3SOfbaUhqfw8KVJ6CEnhu/72b7+6kTWY2D7jNnUJIVNAR\nDgR+aGZnI2/c1+TaPBD7tkv8UU04x8weR+rndelvCcxHduHbUUzbde2+pxOI3xhkPphmSib8NWSL\nKoJ9gRGu5rsZEczSkFv/Ec5C0sl2SNbaHORl+SXE5JXel/z928xsupltRXvTvnXirwQqoNajkPfM\nQ0hKuYaMi06ljGnIY+Qx4L1VUFm04UTVxpuQUfhKZAA+DG0aF4fBXMWRyNX8M7jjQi+4c9d7Oe5f\nDfE/h3ofPEQu+0JFbZjh728rIaIJuA3ZD0uleiFRsyKueyYi/tP93oko1iSVbuPzVyPiNC3/Wzf4\nk/PRiCP8AVLrbSBzAshLc39B8XArKhiDAR/7y5F9bxJSpS1ksJT7Iu/7W+gxm8kQ7cjjmo3skjeh\nDCobyFRq27OZoITK21AYQplUUNslQV+X45BH3U3ItXykt+PSgrE7Ddmn02wTvQbspnvOS5Gt628M\nIR0gZulZFLNYOi1b0g/m350606xAzksj8u1G2pbfxnlbFn9yPSFdD+36rSr8/TjKdsAIZAB/xK/H\nIA+dlWQqrbgxzfKJMKbd+7qckEXqvLUorc2hfj3DO35yig8Rzf2qmAgocDd6D81HXOmC9NuTZ/fx\nhdMTcUzeky7ysci7cpJfb8bT8VDgNYgI1fvbva+LNqSqpSn+d3e0OW1CDgKLyVQJ+b5Ivc56Dt5O\n3vE54AmygNnXIweKmMMvjv2LEGM1tuhbOsBzFZ6+C/fOQ5vRWjKm6N0ovib9xoh/dpXfXdC+mDvz\nEuBdfn4McsN/f8H8WQzMSdvZbZty7xuZnK9DnrzT/PrlyCU/elTGPpmUe1/X+0KuDQsQUTzPv+c8\nPNE1BWYGFCIwSOXZSxsYzDBMR84oJyOm/h3AuqLvRMx2WzVtB/jzuKO33mQUmxi9GAvNDGXx9/Po\ntUMOTCbbcageS3RvXoTUKW8u6jy/94Ki+x3gnUJrFPoBvhjORRvjXiiC/0iyRIrfoJpEmcfQyhkd\niyTILUiKfLffvxQ5RUR348rLkCRtOAlJjd9D3NACZGd4BsWfpYux0KmhS3yjaM2uMBZJRg8jF965\nfn85crs+A3lLTRrinb0yK6kk8yo//03Sht0Q0YzpsSqZh8i+9CRSoX4VeK3fn4eIwSLESH3D+6Fd\nUtkqsu0P5P6eTObivYEsF9t4pGa9m4xwvzD3rq5tY+la9Ov3IUbtIygt2J7Iy/UwMmnu2yShAmX6\nBKmwFqFUSrEPDscl2tyzj5DtSbtVNQaxf3PX85Gq8Uxv42FoT/ysr6FHSUrDFK3NXueCny9AquUv\n+rhPR2EvR+X+J+7hlWY46cexQxuVmV1lZh/28+luX7oBRfjPDiF8B3GPMSj3ARS8eZyZ7R28J5L3\nDQS5Pbfc30EbRpiyCfwID041RZJvRlzzn5Bu+TnksHEGkqbwez/qFFcb/HsildJKM3ux656PRh5c\n70KE+0NmNgUtxHG0ZrYoBWY238z2T65Hm9kZiLtfHkJY4HhPRQthFfKkBA/QC4mNwe2BFrqwhZjZ\nVNTP1zn+XdDC+ytaGFOR3XFEUAXkL6M+GoMkrULotA0+Dy/z8z39e/6BmJfXBrnT3oySHYOYp1vQ\nPJxVMA+th3loQdksvoPi4WJ9NIKyRvwB2eReiIjWUsQ4kMcTKnA3T8Z0V/+7CwpkfzWS8Gb4GvwX\nWgejEUdPUNhC+q7QxVjMN7N7gPmm4F3M7DQUG7UYBamvRur1+5FdZIEpSHRPxNgUfU+nWS0GTAHb\n96H1t97xgRwo/hxCuNufHen3V6E1uha4w1QUs9QY+L50KnCBrwfM7O2IUd2KJJk7kev/eSh+70ok\nVW5fE6FHV28zm2pmM81sTHLvKGQDXYKY16vQGnkG2fDnmtkebsc/yfHXVam6d+iAUs9BHOR4FF+0\nwu/fi4jSSCTZPIyndUf68Xk7eneHnMJCZFNYTWvA7nIk1cxG3MO1fn93FKP1XWSc3UiP4iwJx4o2\n/juBT/m9cd62x5CqZy1wo/92PlJFlRaj0Ub3RyQ1RVWO+Xf/mSwv2b5o83yrX29D6YYq42r8+3+C\n2xhRto190KLciJiG1cnzE1GsWkv+uh5x70iSWej98nOU+QG0Mc/uFWdBG6JdZw+UweRkxKm+3e+/\nBsVJxTUyreL+n09Sf8zX3jlkHmsDPgcuQmq2T/q8OQER1zXe3p6KLZJlfP8xInijyZI0X41UR59E\nWo0T/f4Eb8NmpBJ+SwX9cKa/L2oLXur9/kYUsLyG1kD1qF15PbLJDQqoLjEXTkEaldf59WW0Jjn4\nOvC5OB+QlP88JWyUSJ25CqVY+ybK7BILic7zNpyHpNuL/P4+yJFjK5IuK6sVVcfR6WB8nSybwuE+\nEdcg4hQj+lcC91feQGV22JZcz0Wc23LEJW7Bi8chVdSAL6Jr6bH6rE/ox8mi9XdFUuTb0IYcbVAf\nAZb6+TkoRdERvjhL2aGStkxA0tLpSDJclizQ84FbkmdvJFNBlo0/ebGPcezbiX79HsSpxWSql5KV\nojgLEc9pyXuuoUSW+dw8vNXHewmwPvl9JWIUBpAU80sGq9qqKoUR+/5yZA+dh5iVmYiTXk9rccmq\n8LZjWI7wPpnv945CeSwXeX98AKnHZyJbzCDX7y7a8BJga9G3IanpPyROQr5OR6HNfAtJXa9e+wVP\noYSr9sjqeL3D7x+CGNRzfO3MQmt3VkXjcAIi1DG8IVYg/wSSaK9P+xjtX98lc0kfQ05V2CX+Rcjm\ntQo57oxGDNJTPhdPREkGbiCzUU0kK80ytQz+4Tp21Clxg5joHTENOBu4wu+fhfKi7ecDcHD6f5U1\nUhNwM5k95DhfNNs9hZCN4iZy5UJ6xPcqJJH81CdmrG65FqnXNvhzNyPOZRHiND9Kf4ID16OYm8OR\n3vlSpF7aGxGvtT5BHwNO8P9psV/0gPMU74N7yOK+rkFefGeTpcL6CvLaiqma7ibjpo9F3nVliWa3\nkkzlY9CmXb9Hm/8ypIaqvLJrgquIYYn1oD5IK+G+F8/b6NfjkafZL4DTSrRhb58Pc30Nnu1j/jpE\nlLaSSRZneJ+80q/v8+dLxQv6uzaSpVdKHZseRUzmLJ+rdyHp4dSyOBMcs31dPI6YgEOQlH8tkuim\nIpVndOhaRq5MSUn8ecY9Os+ciwSICYhQL0MS90xEWPtSGqauo5OOiRzkKsRBnoX0rAcgMf8evDKu\nP1d5Dijv/KdxqS65f6pPmM8jPXBlGwUiPL9Aetx1Pvkv8QV5K1I1HYLS3/8KOKVvg6TNMIrw70Ob\n9WcQAV2C1F03UpIgFOC93d+9AhHklyGd95Fo03yZ909Mu/NpWp1d9qZHNdMQ87BTSaZvucjImIAl\nwC/9fMgA84rwtmNYpqLQjMsQ0bgzblT+f8cjBqOUystxnYkI9M98Dt7j6+FDyOHoPiT1bQWOSP53\nNj1mfClox5mIEEWvyyitXAlcmDxXqojjEPivR16Fi319LESEYjUyCZyL1NN3IGb3+Irxf5VM3Zt6\nMf7B18UrkP34LpTYu2fmZGc5uu2gJ5DUcrFvTKW96brAvRIvNOcLJm4WByBOZp+K8U1ABOFgXwCP\nktXJOQXZ5yrZhDtoy+lInbMJifXLkG76S0ji+xhZfEpPHpVt8L7S+2A/RJi2eF+8AHGTm5K+StMe\n9dWLiBolmSHaEKW87wEn+3nXrt1d4mzHsMQqu5uRRJvPdlEp4fQ1MZosLGMFcJWfj8zNhUEu2xXg\nj15sH8jd3wQcU8PY7440TFMQY/AFFBu5Dk9A7WvijX3G/3K/jkms15PEQFGTZqGOo9OOiURhMfBE\n7Kzk975wkAXt+B1e+ZUKVAgd4FsN3OnnS5EEGVVuy6lJ14tcrZ/EjbJ+bzpSwYxAqsc7SKrzVoh7\nC5IaxyIV42bffA5GnOX+yaZdeVxQm3lYqyQzRHvGU3HF6R3gyzMsSxHD8hWkCk+zkXed9qhEu9aT\ni83r93j4nH8I2YnfgAj0XSRu333+5lXAfX4+BkkwTyFJc78a8F8BPJi7dzteoeB/7eimY+Jm9H0y\nDrIy7r3DNiwBnq+1g8S9x6qnMe6gtm9O2rGGrPx1PlBwfL+IJrILPUVmf4zxWcMSe8EwSDJDtGUu\nkmbrYtSKGJYDySWO7Xd7fN3vj2xOUZKY3E+cbdpxJF6hGy8fXzP+3+FejIhJO5okeLom/PNQEPvd\nKHZut7rw13l0XHAshBA8f9nTqGwCoWb/+xDCrabKoAPepFAD2gsRF7tLUMVPasKbhwOAUUV56ILi\nZPoCIYQnzWwNkqRmhBB+4/djLrmBUGPK/2QePkM2D0vnxuuxLfdSUIqkj/j+aWbrkA0q5jj8NaqC\nmz7X1/4Iqug7HtlrL/B+2J6rsJ+4c+14EHiwbrwJXIgcqm7zNXB/zfgvQILDT1EGmBtrxl8bdFUZ\nE9ksfo7XrhkOCCFcUzO+jWY2uWbiWARLQwh/Hw7EIYTLPYntJODJlDDVSaQSGPZ5OIxQyLAMA5F4\nBAXbxuSrZRJKl23LsKzJYWKcU/ybzGxX5PH5XJ246wYbvn23gV6gbgmmgZ0LzGz34WJYiqCZjw3U\nAQ2haqBjcFXTsHDNDbRCQyAa+H+ChlA10EADDTSwU0OpwokNNNBAAw000G9oCFUDDTTQQAM7NTSE\nqoEGGmiggZ0aGkLVQAMNNNDATg0NoWqggQYaaGCnhoZQNdBAAw00sFPDfwHfz+7n1Rd25gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e41fe210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "plt.plot(portfolio.time_grid, path_gbm, 'r', label='gbm')\n",
    "plt.plot(portfolio.time_grid, path_jd, 'b', label='jd')\n",
    "plt.xticks(rotation=30)\n",
    "plt.legend(loc=0); plt.grid(True)\n",
    "# tag: dx_portfolio_2\n",
    "# title: Highly correlated risk factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,  39.28773263,  23.82860521, ...,   0.        ,\n",
       "         6.3226358 ,   7.94977565])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv1 = 5 * port_corr.valuation_objects['eur_call_pos'].\\\n",
    "            present_value(full=True)[1]\n",
    "pv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.05537489,  10.91479057,  20.26091821, ...,  21.69302419,\n",
       "        17.76714942,   0.        ])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv2 = 3 * port_corr.valuation_objects['am_put_pos'].\\\n",
    "            present_value(full=True)[1]\n",
    "pv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f19e43b1450>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5c+jYsaNv27p169i/f7/vvoqKBXhPWA9U6yk0/UVHR/PSSy+xaNGiM06TWVJoaCgDBw6k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\nWbQgKKWUAuD/A+rJVVUw3aM0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e43f4d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([pv1, pv2], bins=25,\n",
    "         label=['European call', 'American put']);\n",
    "plt.axvline(pv1.mean(), color='r', ls='dashed',\n",
    "            lw=1.5, label='call mean = %4.2f' % pv1.mean())\n",
    "plt.axvline(pv2.mean(), color='r', ls='dotted',\n",
    "            lw=1.5, label='put mean = %4.2f' % pv2.mean())\n",
    "plt.xlim(0, 80); plt.ylim(0, 10000)\n",
    "plt.grid(); plt.legend()\n",
    "# tag: dx_portfolio_3\n",
    "# title: Frequency distributions of option position present values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f19e40f7d90>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G9GIaEZG+J5eD/1VX3ZxouzfeeIxnn93JiBEjejnRB0VRREMQH2hGWQdIJJT+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\nCmnwFxGpQhr8RUSqkAZ/EZEq9P8Bl34Piy+hPn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e4569990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvs = pv1 + pv2\n",
    "plt.hist(pvs, bins=50, label='portfolio');\n",
    "plt.axvline(pvs.mean(), color='r', ls='dashed',\n",
    "            lw=1.5, label='mean = %4.2f' % pvs.mean())\n",
    "plt.xlim(0, 80); plt.ylim(0, 7000)\n",
    "plt.grid(); plt.legend()\n",
    "# tag: dx_portfolio_4\n",
    "# title: Portfolio frequency distribution of present values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.736290069957963"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# portfolio with correlation\n",
    "pvs.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.71542409437863"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# portfolio without correlation\n",
    "pv1 = 5 * portfolio.valuation_objects['eur_call_pos'].\\\n",
    "            present_value(full=True)[1]\n",
    "pv2 = 3 * portfolio.valuation_objects['am_put_pos'].\\\n",
    "            present_value(full=True)[1]\n",
    "(pv1 + pv2).std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://shop.oreilly.com/product/0636920032441.do\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
